Merge pull request #191443 from MicrosoftDocs/repo_sync_working_branch

huypub · web-flow · commit a70a4e80c937 · 2022-03-11T15:01:12.000-08:00
Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-docs (branch main)
diff --git a/articles/app-service/monitor-instances-health-check.md b/articles/app-service/monitor-instances-health-check.md
@@ -54,7 +54,7 @@ In addition to configuring the Health check options, you can also configure the
 
 Health check integrates with App Service's [authentication and authorization features](overview-authentication-authorization.md). No additional settings are required if these security features are enabled.
 
-If you're using your own authentication system, the Health check path must allow anonymous access. To secure the Health check endpoint, you should first use features such as [IP restrictions](app-service-ip-restrictions.md#set-an-ip-address-based-rule), [client certificates](app-service-ip-restrictions.md#set-an-ip-address-based-rule), or a Virtual Network to restrict application access. You can secure the Health check endpoint by requiring the `User-Agent` of the incoming request matches `HealthCheck/1.0`. The User-Agent can't be spoofed since the request would already secured by prior security features.
+If you're using your own authentication system, the Health check path must allow anonymous access. To secure the Health check endpoint, you should first use features such as [IP restrictions](app-service-ip-restrictions.md#set-an-ip-address-based-rule), [client certificates](app-service-ip-restrictions.md#set-an-ip-address-based-rule), or a Virtual Network to restrict application access. You can secure the Health check endpoint by requiring the `User-Agent` of the incoming request matches `HealthCheck/1.0`. The User-Agent can't be spoofed since the request would already be secured by prior security features.
 
 ## Monitoring
 
diff --git a/articles/machine-learning/azure-machine-learning-release-notes.md b/articles/machine-learning/azure-machine-learning-release-notes.md
@@ -37,7 +37,7 @@ __RSS feed__: Get notified when this page is updated by copying and pasting the
   + **azureml-synapse**
     +  Fix the issue that magic widget is disappeared.
   + **azureml-train-automl-runtime**
-    +  Updating AutoML dependencies to support python 3.8. This change will break compatibility with models trained with SDK 1.37 or below due to newer Pandas interfaces being saved in the model.
+    +  Updating AutoML dependencies to support Python 3.8. This change will break compatibility with models trained with SDK 1.37 or below due to newer Pandas interfaces being saved in the model.
     +  Automl training now supports numpy version 1.19
     +  Fix automl reset index logic for ensemble models in automl_setup_model_explanations API
     +  In automl, use lightgbm surrogate model instead of linear surrogate model for sparse case after latest lightgbm version upgrade
@@ -1092,7 +1092,7 @@ Learn more about [image instance segmentation labeling](how-to-label-data.md).
     + Throw exception and clean up workspace and dependent resources if workspace private endpoint creation fails.
     + Support workspace sku upgrade in workspace update method.
   + **azureml-datadrift**
-    + Update matplotlib version from 3.0.2 to 3.2.1 to support python 3.8.
+    + Update matplotlib version from 3.0.2 to 3.2.1 to support Python 3.8.
   + **azureml-dataprep**
     + Added support of web url data sources with `Range` or `Head` request. 
     + Improved stability for file dataset mount and download.
@@ -1460,7 +1460,7 @@ Learn more about [image instance segmentation labeling](how-to-label-data.md).
   + **azureml-datadrift**
     + Data Drift results query from the SDK had a bug that didn't differentiate the minimum, maximum, and mean feature metrics, resulting in duplicate values. We have fixed this bug by prefixing target or baseline to the metric names. Before: duplicate min, max, mean. After: target_min, target_max, target_mean, baseline_min, baseline_max, baseline_mean.
   + **azureml-dataprep**
-    + Improve handling of write restricted python environments when ensuring .NET Dependencies required for data delivery.
+    + Improve handling of write restricted Python environments when ensuring .NET Dependencies required for data delivery.
     + Fixed Dataflow creation on file with leading empty records.
     + Added error handling options for `to_partition_iterator` similar to `to_pandas_dataframe`.
   + **azureml-interpret**
@@ -1591,7 +1591,7 @@ Access the following web-based authoring tools from the studio:
 ### Azure Machine Learning SDK for Python v1.2.0
 
 + **Breaking changes**
-  + Drop support for python 2.7
+  + Drop support for Python 2.7
 
 + **Bug fixes and improvements**
   + **azure-cli-ml**
@@ -1623,7 +1623,7 @@ Access the following web-based authoring tools from the studio:
 
 + **Feature deprecation**
   + **Python 2.7**
-    + Last version to support python 2.7
+    + Last version to support Python 2.7
 
 + **Breaking changes**
   + **Semantic Versioning 2.0.0**
@@ -1827,7 +1827,7 @@ Access the following web-based authoring tools from the studio:
   + **azureml-contrib-interpret**
     + Removed text explainers from azureml-contrib-interpret as text explanation has been moved to the interpret-text repo that will be released soon.
   + **azureml-core**
-    + Dataset: usages for file dataset no longer depend on numpy and pandas to be installed in the python env.
+    + Dataset: usages for file dataset no longer depend on numpy and pandas to be installed in the Python env.
     + Changed LocalWebservice.wait_for_deployment() to check the status of the local Docker container before trying to ping its health endpoint, greatly reducing the amount of time it takes to report a failed deployment.
     + Fixed the initialization of an internal property used in LocalWebservice.reload() when the service object is created from an existing deployment using the LocalWebservice() constructor.
     + Edited error message for clarification.
@@ -2364,7 +2364,7 @@ The Experiment tab in the [new workspace portal](https://ml.azure.com) has been
     + Exception will be thrown out when either coarse grain or fine grained timestamp column is not included in keep columns list with indication for user that keeping can be done after either including timestamp column in keep column list or call with_time_stamp with None value to release timestamp columns.
     + Added logging for the size of a registered model.
   + **azureml-explain-model**
-    + Fixed warning printed to console when "packaging" python package is not installed: "Using older than supported version of lightgbm, please upgrade to version greater than 2.2.1"
+    + Fixed warning printed to console when "packaging" Python package is not installed: "Using older than supported version of lightgbm, please upgrade to version greater than 2.2.1"
     + Fixed download model explanation with sharding for global explanations with many features
     + Fixed mimic explainer missing initialization examples on output explanation
     + Fixed immutable error on set properties when uploading with explanation client using two different types of models
@@ -2464,7 +2464,7 @@ At the time, of this release, the following browsers are supported: Chrome, Fire
   + **azureml-pipeline-core**
     + Added support to create, update, and use PipelineDrafts - can be used to maintain mutable pipeline definitions and use them interactively to run
   + **azureml-train-automl**
-    + Created feature to install specific versions of gpu-capable pytorch v1.1.0, :::no-loc text="cuda"::: toolkit 9.0, pytorch-transformers, which is required to enable BERT/ XLNet in the remote python runtime environment.
+    + Created feature to install specific versions of gpu-capable pytorch v1.1.0, :::no-loc text="cuda"::: toolkit 9.0, pytorch-transformers, which is required to enable BERT/ XLNet in the remote Python runtime environment.
   + **azureml-train-core**
     + Early failure of some hyperparameter space definition errors directly in the sdk instead of server side.
 
@@ -2511,9 +2511,9 @@ At the time, of this release, the following browsers are supported: Chrome, Fire
     + Improve reliability of API calls be expanding retries to common requests library exceptions.
     + Add support for submitting runs from a submitted run.
     + Fixed expiring SAS token issue in FileWatcher, which caused files to stop being uploaded after their initial token had expired.
-    + Supported importing HTTP csv/tsv files in dataset python SDK.
+    + Supported importing HTTP csv/tsv files in dataset Python SDK.
     + Deprecated the Workspace.setup() method. Warning message shown to users suggests using create() or get()/from_config() instead.
-    + Added Environment.add_private_pip_wheel(), which enables uploading private custom python packages `whl`to the workspace and securely using them to build/materialize the environment.
+    + Added Environment.add_private_pip_wheel(), which enables uploading private custom Python packages `whl`to the workspace and securely using them to build/materialize the environment.
     + You can now update the TLS/SSL certificate for the scoring endpoint deployed on AKS cluster both for Microsoft generated and customer certificate.
   + **azureml-explain-model**
     + Added parameter to add a model ID to explanations on upload.
diff --git a/articles/machine-learning/component-reference/designer-error-codes.md b/articles/machine-learning/component-reference/designer-error-codes.md
@@ -1594,7 +1594,7 @@ To get more help, we recommend that you post the detailed message that accompani
 
 ## Execute Python Script component
 
-Search **in azureml_main** in **70_driver_logs** of **Execute Python Script component** and you could find which line occurred error. For example, "File "/tmp/tmp01_ID/user_script.py", line 17, in azureml_main" indicates that the error occurred in the 17 line of your python script.
+Search **in azureml_main** in **70_driver_logs** of **Execute Python Script component** and you could find which line occurred error. For example, "File "/tmp/tmp01_ID/user_script.py", line 17, in azureml_main" indicates that the error occurred in the 17 line of your Python script.
 
 ## Distributed training
 
diff --git a/articles/machine-learning/component-reference/execute-python-script.md b/articles/machine-learning/component-reference/execute-python-script.md
@@ -199,7 +199,7 @@ The Execute Python Script component contains sample Python code that you can use
     > [!IMPORTANT]
     > Please use unique and meaningful name for files in the script bundle since some common words (like `test`, `app` and etc) are reserved for built-in services.
    
-    Following is a script bundle example, which contains a python script file and a txt file:
+    Following is a script bundle example, which contains a Python script file and a txt file:
       
     > [!div class="mx-imgBorder"]
     > ![Script bundle example](media/module/python-script-bundle.png)  
@@ -221,7 +221,7 @@ The Execute Python Script component contains sample Python code that you can use
         # Execution logic goes here
         print(f'Input pandas.DataFrame #1: {dataframe1}')
  
-        # Test the custom defined python function
+        # Test the custom defined Python function
         dataframe1 = my_func(dataframe1)
  
         # Test to read custom uploaded files by relative path
@@ -257,7 +257,7 @@ The Execute Python Script component contains sample Python code that you can use
 
     If the component is completed, check the output if as expected.
 
-    If the component is failed, you need to do some troubleshooting. Select the component, and open **Outputs+logs** in the right pane. Open **70_driver_log.txt** and search **in azureml_main**, then you could find which line caused the error. For example, "File "/tmp/tmp01_ID/user_script.py", line 17, in azureml_main" indicates that the error occurred in the 17 line of your python script.
+    If the component is failed, you need to do some troubleshooting. Select the component, and open **Outputs+logs** in the right pane. Open **70_driver_log.txt** and search **in azureml_main**, then you could find which line caused the error. For example, "File "/tmp/tmp01_ID/user_script.py", line 17, in azureml_main" indicates that the error occurred in the 17 line of your Python script.
 
 ## Results
 
diff --git a/articles/machine-learning/how-to-create-register-datasets.md b/articles/machine-learning/how-to-create-register-datasets.md
@@ -296,7 +296,7 @@ partition_keys = new_dataset.partition_keys # ['country']
 
 After you're done wrangling your data, you can [register](#register-datasets) your dataset, and then load it into your notebook for data exploration prior to model training.
 
-For FileDatasets, you can either **mount** or **download** your dataset, and apply the python libraries you'd normally use for data exploration. [Learn more about mount vs download](how-to-train-with-datasets.md#mount-vs-download).
+For FileDatasets, you can either **mount** or **download** your dataset, and apply the Python libraries you'd normally use for data exploration. [Learn more about mount vs download](how-to-train-with-datasets.md#mount-vs-download).
 
 ```python
 # download the dataset 
diff --git a/articles/machine-learning/how-to-debug-managed-online-endpoints-visual-studio-code.md b/articles/machine-learning/how-to-debug-managed-online-endpoints-visual-studio-code.md
@@ -35,7 +35,7 @@ The following table provides an overview of scenarios to help you choose what wo
 
 | Scenario | Inference HTTP Server | Local endpoint |
 |--|--|--|
-| Update local python environment, **without** Docker image rebuild | Yes | No |
+| Update local Python environment, **without** Docker image rebuild | Yes | No |
 | Update scoring script | Yes | Yes |
 | Update deployment configurations (deployment, environment, code, model) | No | Yes |
 | VS Code Debugger integration | Yes | Yes |
diff --git a/articles/machine-learning/how-to-debug-pipelines.md b/articles/machine-learning/how-to-debug-pipelines.md
@@ -209,11 +209,11 @@ run.log("scalar_value", 0.95)
 # Python print statement
 print("I am a python print statement, I will be sent to the driver logs.")
 
-# Initialize python logger
+# Initialize Python logger
 logger = logging.getLogger(__name__)
 logger.setLevel(args.log_level)
 
-# Plain python logging statements
+# Plain Python logging statements
 logger.debug("I am a plain debug statement, I will be sent to the driver logs.")
 logger.info("I am a plain info statement, I will be sent to the driver logs.")
 
diff --git a/articles/machine-learning/how-to-deploy-inferencing-gpus.md b/articles/machine-learning/how-to-deploy-inferencing-gpus.md
@@ -142,7 +142,7 @@ The conda environment file specifies the dependencies for the service. It includ
 ```yaml
 name: project_environment
 dependencies:
-  # The python interpreter version.
+  # The Python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
 - python=3.6.2
 
diff --git a/articles/machine-learning/how-to-designer-python.md b/articles/machine-learning/how-to-designer-python.md
@@ -40,7 +40,7 @@ This article uses the sample dataset, **Automobile price data (Raw)**.
 
 1. Connect the output port of the dataset to the top-left input port of the **Execute Python Script** component. The designer exposes the input as a parameter to the entry point script.
     
-    The right input port is reserved for zipped python libraries.
+    The right input port is reserved for zipped Python libraries.
 
     ![Connect datasets](media/how-to-designer-python/connect-dataset.png)
         
diff --git a/articles/machine-learning/how-to-differential-privacy.md b/articles/machine-learning/how-to-differential-privacy.md
@@ -38,7 +38,7 @@ Use pip to install the [SmartNoise Python packages](https://pypi.org/project/ope
 
 `pip install opendp-smartnoise`
 
-To verify that the packages are installed, launch a python prompt and type:
+To verify that the packages are installed, launch a Python prompt and type:
 
 ```python
 import opendp.smartnoise.core
diff --git a/articles/machine-learning/how-to-inference-server-http.md b/articles/machine-learning/how-to-inference-server-http.md
@@ -118,7 +118,7 @@ The following table contains the parameters accepted by the server:
 
 The following steps explain how the Azure Machine Learning inference HTTP server works handles incoming requests:
 
-1. A python CLI wrapper sits around the server's network stack and is used to start the server.
+1. A Python CLI wrapper sits around the server's network stack and is used to start the server.
 1. A client sends a request to the server.
 1. When a request is received, it goes through the [WSGI](https://www.fullstackpython.com/wsgi-servers.html) server and is then dispatched to one of the workers.
     - [Gunicorn](https://docs.gunicorn.org/) is used on __Linux__.
